IEEE CIS Task Force on Multi-Objective Evolutionary Algorithms
Members of the Task Force Group
- Carlos A. Coello Coello (Chair)
- Marco Laumanns
- Katya Rodríguez-Vázquez
- Ruhul Sarker
- Yaochu Jin
- Lyndon While
- Philip Hingston
Why a Tasking Force Group?
The main aim of the task force groups of
the IEEE CIS Technical Committee
on Evolutionary Computation is to promote the research and
applications in multi-objective evolutionary algorithms (MOEAs).
What is Multi-Objective Optimization?
Multi-objective optimization refers to the solution of problems
with two or more objectives to be satisfied simultaneously. Normally,
such objectives are in conflict with each other and are expressed
in different units. Because of their nature, multi-objective
optimization problems normally have not one but a set of solutions,
which are called Pareto optimal solutions. When such solutions are
plotted in objective function space, the graph produced is called
the Pareto front of the problem.
Why Multi-Objective Evolutionary Algorithms
Despite the existence of numerous mathematical programming techniques
for multi-objective optimization, evolutionary algorithms are particularly
suitable for these problems because of several reasons:
- Evolutionary algorithms are less susceptible to the shape
or continuity of the Pareto front, whereas many mathematical programming
techniques rely on some a priori knowledge about such shape.
- Evolutionary algorithms are population-based. Thus, it is expected
that they can produce several elements of the Pareto optimal set within
a single execution. In contrast, mathematical programming techniques
normally produce a single solution per run.
- Evolutionary algorithms start with a set of random solutions, whereas
mathematical programming techniques normally require a starting point and
the result that they produce tends to rely on such point.
Where can I start to learn about Multi-Objective Evolutionary Algorithms
There are several tutorials available in electronic format. The
following is a representative list:
- A Tutorial on Evolutionary Multiobjective Optimization (2003),
by Eckart Zitzler (tutorial slides).
Note that there is also a paper version of this tutorial. The full reference
is the following:
Eckart Zitzler, Marco Laumanns and Stefan Bleuler. A Tutorial on Evolutionary Multiobjective Optimization, in Xavier Gandibleux, Marc Sevaux, Kenneth Sörensen and Vincent T'kindt (editors), Metaheuristics for Multiobjective Optimisation, pp. 3--37, Springer. Lecture Notes in Economics and Mathematical Systems Vol. 535, Berlin, 2004.
- A Short Tutorial on Evolutionary Multiobjective Optimization (2001),
by Carlos A. Coello Coello (tutorial slides).
Note that there is also a paper version of this tutorial. The full reference
is the following:
Carlos A. Coello Coello. A Short Tutorial on Evolutionary Multiobjective Optimization. In Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David Corne, editors, First International Conference on Evolutionary Multi-Criterion Optimization, pages 21-40. Springer-Verlag. Lecture Notes in Computer Science No. 1993, 2001.
Additional Resources
If you want to get more in-depth knowledge about multi-objective
evolutionary algorithms, there are two monographs devoted to this
topic currently available:
- Carlos A. Coello Coello, David A. Van Veldhuizen and Gary B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic Publishers, New York, March 2002, ISBN 0-3064-6762-3.
- Kalyanmoy Deb. Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, Chichester, UK, 2001, ISBN 0-471-87339-X.
Additionally, the chair of this workgroup has maintained since late 1998 the
EMOO repository, which is located at:
http://delta.cs.cinvestav.mx/~ccoello/EMOO
The EMOO repository contains a lot of valuable resources for
those interested in multi-objective evolutionary algorithms, such as:
- Over 2500 bibliographic references, many of which are electronically
available. This includes about 150 PhD theses, about 650 journal papers
and about 1400 conference papers.
- Contact information of about 65 researchers working in this area.
- Public-domain versions of several multi-objective evolutionary
algorithms, including MOGA, MOPSO, NSGA, NSGA-II, microGA, among others.
- Test functions and sample Pareto fronts for well-known problems.
Events
Some important upcoming events related to multi-objective evolutionary
algorithms are the following:
For additional information
If you want to know more about the activities of this working group,
or you want to join us, please contact:
Dr. Carlos A. Coello Coello
CINVESTAV-IPN
Departamento de Computación
Av. IPN No. 2508
Col. San Pedro Zacatenco
México, D.F. 07360
MEXICO
email: ccoello@cs.cinvestav.mx
URL: http://delta.cs.cinvestav.mx/~ccoello
Tel. +52 55 5061 3800 x 6564
Fax +52 55 5061 3757